class KijkoSmitTest(unittest.TestCase): def setUp(self): """ This generates a catalogue to be used for the regression. """ # Generates a data set assuming b=1 self.dmag = 0.1 mext = np.arange(4.0,7.01,0.1) self.mval = mext[0:-1] + self.dmag / 2.0 self.bval = 1.0 numobs = np.flipud(np.diff(np.flipud(10.0**(-self.bval*mext+7.0)))) # Define completeness window numobs[0:6] *= 10 numobs[6:13] *= 20 numobs[13:22] *= 50 numobs[22:] *= 100 compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]]) self.compl = compl.transpose() numobs = np.around(numobs) magnitude = np.zeros( (np.sum(numobs)) ) year = np.zeros( (np.sum(numobs)) ) * 1999 lidx = 0 for mag, nobs in zip(self.mval, numobs): uidx = int(lidx+nobs) magnitude[lidx:uidx] = mag + 0.01 year_low = compl[0,np.min(np.nonzero(compl[1,:] < mag)[0])] year[lidx:uidx] = (year_low + np.random.rand(uidx-lidx) * (2000-year_low)) lidx = uidx self.catalogue = {'magnitude' : magnitude, 'year' : year} self.ks_ml = KijkoSmit() self.config = {'Average Type' : 'Weighted'} def test_kijko_smith_maximum_likelihood(self): """ Tests that the computed b value corresponds to the same value used to generate the test data set """ bval, sigma_b, aval, sigma_a = self.ks_ml.calculate(self.catalogue, self.config, self.compl) self.assertAlmostEqual(self.bval, bval, 1) def test_kijko_smith_set_reference_magnitude(self): completeness_table = np.array([[1900, 1.0]]) catalogue = {'magnitude': np.array([5.0, 6.0]), 'year': np.array([2000, 2000])} config = {'reference_magnitude' : 0.0} self.ks_ml.calculate(catalogue, config, completeness_table)
def setUp(self): """ This generates a catalogue to be used for the regression. """ # Generates a data set assuming b=1 self.dmag = 0.1 mext = np.arange(4.0,7.01,0.1) self.mval = mext[0:-1] + self.dmag / 2.0 self.bval = 1.0 numobs = np.flipud(np.diff(np.flipud(10.0**(-self.bval*mext+7.0)))) # Define completeness window numobs[0:6] *= 10 numobs[6:13] *= 20 numobs[13:22] *= 50 numobs[22:] *= 100 compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]]) self.compl = compl.transpose() numobs = np.around(numobs) magnitude = np.zeros( (np.sum(numobs)) ) year = np.zeros( (np.sum(numobs)) ) * 1999 lidx = 0 for mag, nobs in zip(self.mval, numobs): uidx = int(lidx+nobs) magnitude[lidx:uidx] = mag + 0.01 year_low = compl[0,np.min(np.nonzero(compl[1,:] < mag)[0])] year[lidx:uidx] = (year_low + np.random.rand(uidx-lidx) * (2000-year_low)) lidx = uidx self.catalogue = {'magnitude' : magnitude, 'year' : year} self.ks_ml = KijkoSmit() self.config = {'Average Type' : 'Weighted'}